Changes between Version 73 and Version 74 of PostGIS_Raster_SoC_Idea_2012/Distance_Analysis_Tools

Timestamp:

Jul 13, 2012, 10:42:51 PM (12 years ago)

Author:

qliu

Comment:

—

PostGIS_Raster_SoC_Idea_2012/Distance_Analysis_Tools

@@ -753 +753 @@
 
 {{{
-'''Comments from Mentor'''
->> Do those algorithms have known names?
-
-> They are called "sequential algorithms" in distance mapping,
+Comments from Mentor
+> Do those algorithms have known names?
+
+  They are called "sequential algorithms" in distance mapping,
 with which the distance surface will ideally be created across the
 entire image(raster) in one scan. ArcGIS also uses sequential
 algorithms in the scanning process.
 
-> The distance for the current pixel
+  The distance for the current pixel
 under scanning is computed using recently computed values from the
 present scan in the neighborhood. For example, in one row scan,
@@ -770 +770 @@
 Dist_row(col) will be replaced with the newly computed distance.
 
-> GRASS's "r.grow.distance" is computing octagonal distance, while GDAL
+  GRASS's "r.grow.distance" is computing octagonal distance, while GDAL
 "gdalproximity" is doing a chessboard scanning manner. GRASS
 "r.grow.distance" creates the distance surface in a "growing" manner
@@ -780 +780 @@
 >> raster?". Is one more efficient than the other one?
 
-> In terms of scanline, both approaches do only one scan for the whole
+  In terms of scanline, both approaches do only one scan for the whole
 raster, row by row.
 In terms of pixels,
@@ -791 +791 @@
 they are using similar algorithms as GDAL.)
 
-> GRASS actually scans columns in each row 4 times. The first time is to
+  GRASS actually scans columns in each row 4 times. The first time is to
 assign distance value of "0" to source pixels. Then 3 times for the
 neighbor to the left, right, and topleft/above/topright.
 
-> So in terms of scanning times, it looks like GDAL is more efficient.
-
->> To which one of those two algorithms our approach is similar (or
->> comparable)?
-
-> Our approach will scan the whole
+  So in terms of scanning times, it looks like GDAL is more efficient.
+
+> To which one of those two algorithms our approach is similar (or
+> comparable)?
+
+  Our approach will scan the whole
 raster only once. So we could use a similar scanning algorithm as GDAL
 in terms of utilizing scanlines to do multiple scans simultaneously.
 
->> Could you describe, in two short sentences, how each of them decide which
->> source is the nearest for each pixel?
-
-> I think both approaches don't have a specific
+> Could you describe, in two short sentences, how each of them decide which
+> source is the nearest for each pixel?
+
+  I think both approaches don't have a specific
 process to actually determine the nearest source pixel to the current
 one, but use this sequential scanning to replace distance with shorter
@@ -814 +814 @@
 be reusable for cost-weighted distance computation. 
 
->> Are you still confident in our approach now that you understand better
->> those two algorithms? Why?
-
-> Yes, I think our approach utilizing KNN indexing will show its
+> Are you still confident in our approach now that you understand better
+> those two algorithms? Why?
+
+  Yes, I think our approach utilizing KNN indexing will show its
 efficiency while dealing with vary large source dataset and very high
 resolution resulted distance raster. Because both methods in GDAL and